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Frailty conceptualises a state of vulnerability due to multiple deficits across several physiological systems.[[1]] It has been shown to predict onset of disability, morbidity, entrance into long-term care (LTC) and mortality.[[1–3]] Identification of frailty can help guide treatments, prognosticate disease, and target resources toward modifiable parameters.[[4]]

There are several approaches to measuring frailty, but most screening tools fit into one, or a combination, of two broad categories: the phenotypic frailty model[[3]] and the cumulative deficit model.[[5–7]] The latter involves generating a frailty index (FI) by summing the deficits an individual has across a range of predetermined medical, functional and social parameters.[[5–7]] With increasing availability of electronic health data, the development of FIs to rapidly assess frailty is attractive.[[8]] Aotearoa New Zealand has been at the forefront of utilising routinely collected, electronically recorded data for FI development. These are attained using International Resident Assessment Instrument (interRAI)[[9]] assessments in those requiring government-funded community supports,[[2,10–12]] with these assessments being performed when patients are relatively well within the community. However, interRAI data are not currently readily accessible, with no access to developed electronic FIs within clinical settings in New Zealand. Additionally, interRAI assessments are only routinely performed in those requiring government-funded community supports, with these data not available for the many individuals requiring healthcare who have not had an interRAI assessment.

Frailty prevalence[[13]] and frailty-specific outcomes in older adults undergoing rehabilitation are not well described in New Zealand. Most rehabilitation units are led by geriatricians with a comprehensive geriatric assessment being integral to care provision, and therefore outcomes for these patients may be different to acutely hospitalised older adults. We wished to develop a tool to measure frailty and assess its predictive validity in the inpatient rehabilitation setting, using routinely electronically collected data.

Methods

This was a retrospective cohort study using routinely collected electronically recorded data in hospitalised adults aged ≥65 years, admitted to a rehabilitation unit at Waitematā District Health Board (WDHB), Auckland, New Zealand, from 8 May 2018 to 31 October 2018. Ethical approval was obtained from the New Zealand Central Health and Disability Ethics Committee (reference 19/CEN/128).

The WDHB rehabilitation service serves older adults aged ≥65 years who have medical and functional needs, providing comprehensive assessment, treatment and goal-directed rehabilitation on individual needs. The service has developed an electronically documented, readily accessible, concise global geriatric assessment—completed as part of the admission process to the inpatient rehabilitation service at WDHB. This electronic document captures active medical problems, comorbidities, medications, living situation, education level, social situation, cognition and mood, drugs and alcohol, vision and hearing impairment, bladder and bowel function, nutrition and appetite, pre-morbid personal and instrumental activities of daily living, and assistance required. It also captures a current 4AT rapid clinical test for delirium score to rapidly screen for delirium and cognitive impairment.[[14]]

Where a patient had more than one electronic admission document completed, only their first admission within the six-month period was included, so each individual was only represented once.

An FI was constructed by extracting variables from the electronic document (see Appendices). The chosen variables were based on previously developed FIs,[[2,5,15]] and study group consensus and encompassed domains of locomotion, sensory, cognition, psychological, function/ADLs and comorbidities. The majority of variables were coded using a binary system where 0 represents absence of the deficit, and 1 represents presence of the deficit. Certain variables were divided into more categories to delineate “degree of deficit”.

The FI numerator is a sum of points scored for each variable included in the FI, divided by the denominator.[[39]] Where information was unavailable these items were excluded from the total denominator, as per usual FI practice. The FI generated a value between 0 and 1, with higher values indicating more severe frailty.

Outcomes were measured one year after index admission by reviewing hospital electronic records and included six-month hospitalisations (primary outcome); one-year mortality; one-year entrance into long-term care (LTC); and 30-day and 90-day hospitalisation (secondary outcomes). Outcome data were sourced from electronic hospital data linked across the wider Auckland Region. The following items were also collected from hospital electronic records: age, gender, ethnicity, living alone/with others, marital status, residence on admission, primary diagnosis, length of stay (LOS) of index admission, discharge destination from index admission (home or LTC [rest home/private hospital/dementia unit]) and if this was a change from residence on admission.

To measure the association between FI and outcomes, participants were allocated into six groups, denoted frailty score (FS) 0,1,2,3,4,5 based on pre-defined FI ranges, consistent with FI application in community-dwelling older adults with health and functional needs, where FS 0=0.00–0.09, FS 1=0.10–0.19, FS 2=0.20–0.29, FS 3=0.30–0.39, FS 4=0.40–0.49, FS 5≥0.50.[[2]] Due to the small numbers of participants with lower FI levels in our study, the first three frailty groups (FS 0, 1 and 2) were combined. Analysis of variance (ANOVA) or Chi-squared tests were used to determine the association between pre-specified FI categories (FS 0–2, 3, 4, 5) and baseline characteristics. The univariate and multivariable association between FI categories (independent variable) and binary outcomes (dependent variables) were explored using logistic regression models with odds ratios (ORs) and 95% confidence intervals (95%CIs). A two-sided p<0.05 was considered statistically significant. All analyses were performed with SAS 9.4 software (SAS Institute Inc., Cary, USA).

Results

A total of 536 electronic admission documents were reviewed in the six-month study period, 369 were excluded as they were completed by non-rehabilitation services (e.g., orthogeriatrics). Five more were excluded as duplicates of the same individuals. In total, 162 participants were included in the analysis (see Figure 1).

Subjects were aged between 66 and 103 years, with a mean (SD)age of 86 (8) years. The median age was 88 years (interquartile range [IQR]=80–92).Two thirds (108) were female. The majority identified as NZ European (143; 88.3%) with 3 (1.9%) being Māori, and the cohort were largely either widowed or married (70, 43.2% and 66, 40.7% respectively). Increasing age was associated with increasing FS (Table 1).

The mean FI of the cohort was 0.42 (SD 0.12), ranging from 0.07 to 0.73, and was approximately normally distributed, as shown in Figure 2. There were 28 (17.3%) participants in FS groups 0–2, 39 (24.1%) in FS 3, 50 (30.9%) in FS 4, and 45 (27.7%) in FS 5, and 147 subjects were considered frail FI>0.25.[[16]]

The group with the highest frailty (FS 5) had the highest rate of being discharged to an increased level of care at 35.6% (n=16), compared with the least frail at 10.7% (n=3). The group with lowest frailty (FS 0–2) had the highest mean LOS at 25.4 days, and the group with the highest frailty (FS 5) had the lowest mean LOS of 15.7 days (p=0.04). Analysis of frailty category LOS by whether inpatients were admitted from LTC or home or whether they were discharged to a higher level of care (i.e., admitted from home, discharged to LTC) found that LOS was significantly shorter for the frailest being discharged to a higher level of care (Table 2).

At six months, a total of 80 (49.4%) participants had at least one hospitalisation. The six-month hospitalisation proportion was significantly different between FI groups; 66.7% in the most frail group compared to 35.7% in the least frail group (FS 0–2). Participants in the most fail group had significantly higher risk of hospitalisation in both unadjusted (OR=3.60; 95%CI=1.34, 9.70; p=0.01) and adjusted (OR=6.19; 95%CI=1.82, 21.13; p=0.004) logistic regressions (Table 3).

The overall one-year mortality proportion was 23% (n=37). One-year mortality in the composite group (FS 0–2) was 3.5% (n=1), significantly lower than FS 5 40.0% (n=18) (p=0.01; Table 3). In the adjusted logistic regression, similar results were observed (OR=14.69; 95%CI=1.58, 136.43; p=0.02).

At baseline 10 (6.2%) resided in LTC, eight of whom were in the most frail group (FS 5), and two participants in FS 3. These participants were excluded in the LTC admission analysis. By the end of the follow up period 51 (33.6%) of the remaining cohort had newly entered LTC. By one year 19 (54.1%) of the most frail group (FS 5) had newly entered LTC, compared to 4 (14.3%) of the composite group (FS 0–2) (p=0.004; Table 3). In the adjusted logistic regression similar results were observed in most frail group (OR=5.12; 95%CI=1.28, 20.43; p=0.02).

There were no significant differences in hospital admission proportion at 30 or 90 days between FI groups (Table 3).

View Tables and Figures.

Discussion

This study reports the use of an FI to determine the prevalence of frailty in the rehabilitation setting and adds to the relatively limited New Zealand literature within this population. It is important that frailty tools used in different settings are shown to be fit for purpose and this FI derived from routinely collected electronic data demonstrated predictive validity in terms of six-month hospitalisation rates, one-year mortality and one-year LTC entry. Predictive validity is an essential component to frailty operationalisation, particularly as there is no gold-standard measurement.[[15,17]] The finding of increased frailty associated with shorter LOS is an unexpected finding and warrants further investigation. As the data sourced are electronically recorded, the potential exists to automatically generate a FS visible, and of use to, admitting clinicians without additional work—a point of difference to other clinically utilised tools.

Our cohort had high average baseline frailty (mean FI 0.42) and very high prevalence of frailty at 90.7% (utilising cut-off of 0.25).[[16]] Distribution was normal, which is to be expected in populations with greater health issues.[[17]] A study from Singapore[[18]] reported prevalence rate of 87% by FI of inpatients in a geriatrics department (although unlike our study, also including acute inpatients), while a study from a single rehabilitation facility in Switzerland 10 reported 44% were frail by FI (median 0.37). In contrast we can utilise mean FI score to compare to other studies, with the mean frailty in our cohort similar to that in two Australian studies[[19,20]] (mean FI 0.42 and 0.46) but higher than a Finnish study (0.34)[[21]] in similar rehabilitation settings. By comparing in this way, it appears older adults undergoing rehabilitation in our cohort sit at the higher end of frailty prevalence compared to international studies. Frailty prevalence in our study was higher than that reported by Richard et al.,[[13]] as far as we are aware the only other publication reporting frailty in the New Zealand rehabilitation setting. In Richard et al.’s point prevalence study of frailty in Christchurch hospital,[[13]] overall ~49% were considered frail by the Reported Edmonton Frailty Scale, increasing to 74% within the rehabilitation wards. Māori were significantly more likely to be frail.

Unsurprisingly, our subjects were found to be frailer than other New Zealand studies assessing frailty by FI in community settings.[[2,10]] The mean frailty by FI in a study of community dwelling older adults in Canterbury assessed for government-funded supports was 0.27, and 0.16 in a population of Auckland retirement village residents. While there are a small number of other New Zealand frailty studies utilising validated tools in specific sub-specialty populations, compared to our international colleagues, we in New Zealand are lacking in reports of frailty. This dearth of information, particularly as it affects our Indigenous population, has been noted.[[22]] Given healthcare costs are approximately five times higher for the frail compared to the non-frail,[[23]] it is essential that feasible strategies to identify those living with frailty are used and effective and appropriate interventions are delivered. From an overall community perspective, one way of potentially achieving this would be by improving accessibility to interRAI data.

Consistent with some, but not all similar reports, our study found association with discharge destination,[[19,20,24]] and one year mortality.[[24]] We found no significant association with short-term hospitalisations at 30 or 90 days. While the number of outcomes was small for 30-day hospitalisations, for 90 days it was comparable to other outcomes that showed significant differences. Other studies have assessed frailty with the need to be readmitted to acute care or the emergency department during their current rehabilitation admission.[[19–24]] This seems to infer medical instability and we did not study this as an outcome. There is little research in terms of outcomes for the rehabilitation population between frailty groups in the immediate post-discharge period; this is an area that requires further scholarship in larger studies. For example, it is possible that no association is found between frailty and short-term re-hospitalisation rates because older rehabilitation patients, usually cared for by geriatricians within a multidisciplinary team, receive a comprehensive geriatric assessment. Lin et al.[[26]] have recently published results showing frail older people are less likely to be readmitted if they received a comprehensive geriatrics assessment during admission. More study is required here, but perhaps geriatrician input prior to discharge eliminates some of the risk of readmissions for those living with a greater degree of frailty.

Our results differ significantly to the literature when assessing frailty association with LOS. Where other studies in both the acute setting[[27,28]] and (the smaller number) in the rehabilitation setting[[19,21]] show association of frailty with longer LOS, our results report the opposite with higher frailty associated with shorter LOS; a surprising result. We had expected the variance here to be explained by the higher number of participants already residing in LTC in the highest frailty group FS 5 (eight participants, out of 45) compared to FS 0–2, which had no participants already residing in LTC, and with high rates of FS 5 discharged to LTC overall, compared with only 7.1% of FS 0–2. Numbers were too small in these groups to show any significance; however, further subgroup analysis of LOS data found that the most frail, who were admitted from the community but discharged to LTC, had significantly shorter LOS, and that LOS decreased as frailty increased in this group. This may be a reflection of the frailest reaching their rehabilitation potential plateau or limits earlier than the less frail, with the decision to discharge made earlier, compared to those continuing to make inpatient gains. However, of potential concern, it may highlight an issue that the most frail and vulnerable population receive less physiotherapy or other allied health involvement due to a perceived lack of benefit. It may also reflect different practices between New Zealand and internationally with regards to the least and most frail patients. This requires further unravelling to ensure that best care is being delivered to all, and illustrates the value of interrogating and reporting frailty and outcomes in different populations and care settings.

Limitations

This study included a relatively small number of subjects from a single centre, and other outcomes may be significant if a larger cohort was included. Despite this, significant important findings were found.

Frailty in the older adult rehabilitation setting is relatively under-explored in comparison to acute hospitalised patients or community dwellers, yet it is an important group to be considered. The value of utilising electronic health data for FI development is the potential for automating FI results into clinical notes. This has potential to increase clinician awareness of this syndrome, including to primary care if FI is incorporated into discharge summaries. It brings frailty to the forefront, allowing focus on frailty-centred care and appropriate distribution of resources with evidence that geriatricians would use such information to inform clinical judgement and individualise care.[[29]]

Future focuses of study are to interrogate why the more frail have shorter LOS in this population by comparing components of frailty management, such as amount of physiotherapy received, between those more and less frail, and also to further investigate frailty level of short-term outcomes/readmission at time of discharge.

View Appendices.

Summary

Abstract

Aim

Frailty within the older adult rehabilitation population is relatively under-explored. We aimed to derive a frailty index (FI) from electronic routinely collected data to determine frailty prevalence, and to assess its ability to predict adverse outcomes in the rehabilitation setting.

Method

A FI was derived and retrospectively applied to electronically recorded health information of older adults admitted for inpatient rehabilitation. For analysis, subjects were allocated into frailty score (FS) groups (0–5). Primary outcome was a six-month hospitalistion rate, and other outcomes were: mortality, entrance into long-term care (LTC) at one year, length of stay (LOS), 30- and 90-day hospitalistions. Univariate and multivariable logistic regressions analysed associations between frailty and outcomes.

Results

One hundred and sixty-two patient electronic notes were reviewed. Mean (SD) age was 86 (8.2) years, 147 (90.7%) were considered frail (FS>0.25). The most frail group (FS 5) had higher risk of six-month hospitalisations (OR=6.19; 95%CI=1.82, 21.13; p=0.004). A higher frailty score was associated with shorter LOS compared to lowest frailty scores (15.7 days vs 25.4 days; p=0.04). No relationship was found with shorter-term outcomes.

Conclusion

Prevalence of frailty is high in the rehabilitation setting. Association of frailty with shorter LOS and lack of association found with shorter-term outcomes warrant further study.

Author Information

Himali Aickin: Waitematā District Health Board, Auckland, New Zealand. Katherine Bloomfield: Waitematā District Health Board, Auckland, New Zealand; The University of Auckland, Auckland, New Zealand. Zhenqiang Wu: The University of Auckland, Auckland, New Zealand. Martin J Connolly: Waitematā District Health Board, Auckland, New Zealand; The University of Auckland, Auckland, New Zealand.

Acknowledgements

Correspondence

Katherine Bloomfield: Waitematā Clinical Campus, North Shore Hospital, PO Box 93 503, Takapuna, Auckland 0740, New Zealand. Ph: +64 9 4868920/42414, Fax: +64 9 4427166

Correspondence Email

Katherine.bloomfield@waitematadhb.govt.nz

Competing Interests

Nil.

1) Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752-762. doi:10.1016/S0140-6736(12)62167-9.

2) Burn R, Hubbard RE, Scrase RJ, et al. A frailty index derived from a standardized comprehensive geriatric assessment predicts mortality and aged residential care admission. BMC Geriatr. 2018;18(1):319. doi:10.1186/s12877-018-1016-8.

3) Fried L, Tangen C, Walston J, et al. Frailty in Older Adults: Evidence for a Phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-M156. doi:10.1093/gerona/56.3.m146.

4) Dent E, Lien C, Lim WS, et al. The Asia-Pacific Clinical Practice Guidelines for the Management of Frailty [published correction appears in J Am Med Dir Assoc. 2018 Jan;19(1):94]. J Am Med Dir Assoc. 2017;18(7):564-575. doi:10.1016/j.jamda.2017.04.018.

5) Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173(5):489-495. doi:10.1503/cmaj.050051.

6) Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. ScientificWorldJournal. 2001;1:323-336. doi:10.1100/tsw.2001.58.

7) Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci. 2007;62(7):722-727. doi:10.1093/gerona/62.7.722.

8) Brousseau AA, Dent E, Hubbard R, et al. Identification of older adults with frailty in the Emergency Department using a frailty index: results from a multinational study. Age Ageing. 2018;47(2):242-248. doi:10.1093/ageing/afx168.

9) Hirdes JP, Ljunggren G, Morris JN, et al. Reliability of the interRAI suite of assessment instruments: a 12-country study of an integrated health information system. BMC Health Serv Res. 2008; 8:277-288. doi: 10.1186/1472-6963-8-277.

10) Bloomfield K, Wu Z, Tatton A, et al. An interRAI-derived frailty index is associated with prior hospitalisations in older adults residing in retirement villages. Australas J Ageing. 2021;40(1):66-71. doi:10.1111/ajag.12863.

11) Bloomfield K, Wu Z, Tatton A, Calvert C, Peel N, Hubbard R, Jamieson H, Hikaka J, Boyd M, Bramley D, Connolly MJ. An interRAI derived frailty index predicts acute hospitalizations in older adults residing in retirement villages: A prospective cohort study. PLoS One. 2022 Mar 2;17(3):e0264715.

12) Abey-Nisbit R, Peel NM, Matthews H, et al. Frailty of Māori, Pasifika and non- Māori/non-Pasifika older people in New Zealand: a national population study of older people referred for home care services. J Gerontol A Biol Sci Med Sci. 2021 May 22;76(6):1101-1107.

13) Richards SJG, D’Souza J, Pascoe R et al. Prevalence of frailty in a tertiary hospital: A point prevalence observational study. PLOS ONE 2019 Jul 1; 14(7):e0219083.

14) Bellelli G, Morandi A, Davis DHJ, et al. Validation of the 4AT, a new instrument for rapid delirium screening: A study in 234 hospitalised older people [published correction appears in Age Ageing. 2015 Jan;44(1):175]. Age Ageing. 2014;43(4):496-502. doi:10.1093/ageing/afu021.

15) Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8:24. doi:10.1186/1471-2318-8-24.

16) Rockwood K, Andrew M, Mitnitski A. A comparison of two approaches to measuring frailty in elderly people. J Gerontol A Biol Sci Med Sci. 2007 Jul;62(7):738-43.

17) Basic D, Shanley C. Frailty in older inpatient population: using the clinical frailty scale to predict patient outcomes. J Aging Health. 2015; 27:670-685.

18) Chong E, Ho E, Baldevarona-Llego J, Chan M, Wu L, Tay L. Frailty and risk of adverse outcomes in hospitalized older adults: a comparison of different frailty measures. J Am Med dir Assoc. 2017 Jul 1;18(7):638.e7-638.e11

19) Kohler S, Rametta R, Poulter M, Vogrin S, Yates P. Resilience, frailty and outcomes in geriatric rehabilitation. Austral J Aging. 2020 Jun;39)2)e205-e209.

20) Arjunan A, Peel NM, Hubbard RE. Feasibility and validity of frailty measurement in geriatric rehabilitation. Australas J Ageing. 2018 Jun;37(2):144-146.

21) Kerminen H, Huhtala H, Jäntti P, Valvanne J, Jämsen E. Frailty Index and functional level upon admission predict hospital outcomes: an interRAI-based cohort study of older patients in post-acute care hospitals. BMC Geriatr. 2020;20(1):160. Published 2020 May 5. doi:10.1186/s12877-020-01550-7.

22) Lewis ET, Howard L, Cardona M, Radford K, Withall A, Howie A, Rockwood K, Peters R. Frailty in Indigenous Populations: A Scoping Review. Front Public Health. 2021 Nov 22;9:785460.

23) Bock JO, König HH, Brenner H, Haefeli WE, Quinzler R, Matschinger H, Saum KU, Schöttker B, Heider D. Associations of frailty with health care costs--results of the ESTHER cohort study. BMC Health Serv Res. 2016 Apr 14;16:128.

24) Stuck AK, Mangold JM, Wittwer R, Limacher A, Bischoff-Ferrari HA. Ability of 3 Frailty Measures to Predict Short-Term Outcomes in Older Patients Admitted for Post-Acute Inpatient Rehabilitation. J Am Med Dir Assoc. 2021 Oct 20:S1525-8610(21)00865-3.

25) Chong E, Ho E, Baldevarona-Llego J, Chan M, Wu L, Tay, Ding YY, Lim WS. Frailty in hospitalized older adults: comparing different frailty measures in predicting short- and long-term patient outcomes. J Am Med Dir Assoc. 2018 May;19(5):450-457.e3.

26) Lin MH, Wang KY, Chen CH, Hu FW. Factors associated with 14-day hospital readmission in frail older patients: a case-control study. Geriatr Nurs. 2022 43;146-150.

27) Singh I, Gallacher J, Davis K, Johansen A, Eeles E, Hubbard RE. Predictors of adverse outcomes on an acute geriatric rehabilitation ward. Age Ageing. 2012;41:242-246.

28) Evans SJ, Sayers M, Mitnitski A, Rockwood K. The risk of adverse outcomes in hospitalized older patients in relation to a frailty index based on a comprehensive geriatric assessment. Age Ageing. 2014;43:127-132.

29) Khatry K, Peel NM, Gray LC, Hubbard RE. The Utility of the Frailty Index in Clinical Decision Making. J Frailty Aging. 2018;7(2):138-141.

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Frailty conceptualises a state of vulnerability due to multiple deficits across several physiological systems.[[1]] It has been shown to predict onset of disability, morbidity, entrance into long-term care (LTC) and mortality.[[1–3]] Identification of frailty can help guide treatments, prognosticate disease, and target resources toward modifiable parameters.[[4]]

There are several approaches to measuring frailty, but most screening tools fit into one, or a combination, of two broad categories: the phenotypic frailty model[[3]] and the cumulative deficit model.[[5–7]] The latter involves generating a frailty index (FI) by summing the deficits an individual has across a range of predetermined medical, functional and social parameters.[[5–7]] With increasing availability of electronic health data, the development of FIs to rapidly assess frailty is attractive.[[8]] Aotearoa New Zealand has been at the forefront of utilising routinely collected, electronically recorded data for FI development. These are attained using International Resident Assessment Instrument (interRAI)[[9]] assessments in those requiring government-funded community supports,[[2,10–12]] with these assessments being performed when patients are relatively well within the community. However, interRAI data are not currently readily accessible, with no access to developed electronic FIs within clinical settings in New Zealand. Additionally, interRAI assessments are only routinely performed in those requiring government-funded community supports, with these data not available for the many individuals requiring healthcare who have not had an interRAI assessment.

Frailty prevalence[[13]] and frailty-specific outcomes in older adults undergoing rehabilitation are not well described in New Zealand. Most rehabilitation units are led by geriatricians with a comprehensive geriatric assessment being integral to care provision, and therefore outcomes for these patients may be different to acutely hospitalised older adults. We wished to develop a tool to measure frailty and assess its predictive validity in the inpatient rehabilitation setting, using routinely electronically collected data.

Methods

This was a retrospective cohort study using routinely collected electronically recorded data in hospitalised adults aged ≥65 years, admitted to a rehabilitation unit at Waitematā District Health Board (WDHB), Auckland, New Zealand, from 8 May 2018 to 31 October 2018. Ethical approval was obtained from the New Zealand Central Health and Disability Ethics Committee (reference 19/CEN/128).

The WDHB rehabilitation service serves older adults aged ≥65 years who have medical and functional needs, providing comprehensive assessment, treatment and goal-directed rehabilitation on individual needs. The service has developed an electronically documented, readily accessible, concise global geriatric assessment—completed as part of the admission process to the inpatient rehabilitation service at WDHB. This electronic document captures active medical problems, comorbidities, medications, living situation, education level, social situation, cognition and mood, drugs and alcohol, vision and hearing impairment, bladder and bowel function, nutrition and appetite, pre-morbid personal and instrumental activities of daily living, and assistance required. It also captures a current 4AT rapid clinical test for delirium score to rapidly screen for delirium and cognitive impairment.[[14]]

Where a patient had more than one electronic admission document completed, only their first admission within the six-month period was included, so each individual was only represented once.

An FI was constructed by extracting variables from the electronic document (see Appendices). The chosen variables were based on previously developed FIs,[[2,5,15]] and study group consensus and encompassed domains of locomotion, sensory, cognition, psychological, function/ADLs and comorbidities. The majority of variables were coded using a binary system where 0 represents absence of the deficit, and 1 represents presence of the deficit. Certain variables were divided into more categories to delineate “degree of deficit”.

The FI numerator is a sum of points scored for each variable included in the FI, divided by the denominator.[[39]] Where information was unavailable these items were excluded from the total denominator, as per usual FI practice. The FI generated a value between 0 and 1, with higher values indicating more severe frailty.

Outcomes were measured one year after index admission by reviewing hospital electronic records and included six-month hospitalisations (primary outcome); one-year mortality; one-year entrance into long-term care (LTC); and 30-day and 90-day hospitalisation (secondary outcomes). Outcome data were sourced from electronic hospital data linked across the wider Auckland Region. The following items were also collected from hospital electronic records: age, gender, ethnicity, living alone/with others, marital status, residence on admission, primary diagnosis, length of stay (LOS) of index admission, discharge destination from index admission (home or LTC [rest home/private hospital/dementia unit]) and if this was a change from residence on admission.

To measure the association between FI and outcomes, participants were allocated into six groups, denoted frailty score (FS) 0,1,2,3,4,5 based on pre-defined FI ranges, consistent with FI application in community-dwelling older adults with health and functional needs, where FS 0=0.00–0.09, FS 1=0.10–0.19, FS 2=0.20–0.29, FS 3=0.30–0.39, FS 4=0.40–0.49, FS 5≥0.50.[[2]] Due to the small numbers of participants with lower FI levels in our study, the first three frailty groups (FS 0, 1 and 2) were combined. Analysis of variance (ANOVA) or Chi-squared tests were used to determine the association between pre-specified FI categories (FS 0–2, 3, 4, 5) and baseline characteristics. The univariate and multivariable association between FI categories (independent variable) and binary outcomes (dependent variables) were explored using logistic regression models with odds ratios (ORs) and 95% confidence intervals (95%CIs). A two-sided p<0.05 was considered statistically significant. All analyses were performed with SAS 9.4 software (SAS Institute Inc., Cary, USA).

Results

A total of 536 electronic admission documents were reviewed in the six-month study period, 369 were excluded as they were completed by non-rehabilitation services (e.g., orthogeriatrics). Five more were excluded as duplicates of the same individuals. In total, 162 participants were included in the analysis (see Figure 1).

Subjects were aged between 66 and 103 years, with a mean (SD)age of 86 (8) years. The median age was 88 years (interquartile range [IQR]=80–92).Two thirds (108) were female. The majority identified as NZ European (143; 88.3%) with 3 (1.9%) being Māori, and the cohort were largely either widowed or married (70, 43.2% and 66, 40.7% respectively). Increasing age was associated with increasing FS (Table 1).

The mean FI of the cohort was 0.42 (SD 0.12), ranging from 0.07 to 0.73, and was approximately normally distributed, as shown in Figure 2. There were 28 (17.3%) participants in FS groups 0–2, 39 (24.1%) in FS 3, 50 (30.9%) in FS 4, and 45 (27.7%) in FS 5, and 147 subjects were considered frail FI>0.25.[[16]]

The group with the highest frailty (FS 5) had the highest rate of being discharged to an increased level of care at 35.6% (n=16), compared with the least frail at 10.7% (n=3). The group with lowest frailty (FS 0–2) had the highest mean LOS at 25.4 days, and the group with the highest frailty (FS 5) had the lowest mean LOS of 15.7 days (p=0.04). Analysis of frailty category LOS by whether inpatients were admitted from LTC or home or whether they were discharged to a higher level of care (i.e., admitted from home, discharged to LTC) found that LOS was significantly shorter for the frailest being discharged to a higher level of care (Table 2).

At six months, a total of 80 (49.4%) participants had at least one hospitalisation. The six-month hospitalisation proportion was significantly different between FI groups; 66.7% in the most frail group compared to 35.7% in the least frail group (FS 0–2). Participants in the most fail group had significantly higher risk of hospitalisation in both unadjusted (OR=3.60; 95%CI=1.34, 9.70; p=0.01) and adjusted (OR=6.19; 95%CI=1.82, 21.13; p=0.004) logistic regressions (Table 3).

The overall one-year mortality proportion was 23% (n=37). One-year mortality in the composite group (FS 0–2) was 3.5% (n=1), significantly lower than FS 5 40.0% (n=18) (p=0.01; Table 3). In the adjusted logistic regression, similar results were observed (OR=14.69; 95%CI=1.58, 136.43; p=0.02).

At baseline 10 (6.2%) resided in LTC, eight of whom were in the most frail group (FS 5), and two participants in FS 3. These participants were excluded in the LTC admission analysis. By the end of the follow up period 51 (33.6%) of the remaining cohort had newly entered LTC. By one year 19 (54.1%) of the most frail group (FS 5) had newly entered LTC, compared to 4 (14.3%) of the composite group (FS 0–2) (p=0.004; Table 3). In the adjusted logistic regression similar results were observed in most frail group (OR=5.12; 95%CI=1.28, 20.43; p=0.02).

There were no significant differences in hospital admission proportion at 30 or 90 days between FI groups (Table 3).

View Tables and Figures.

Discussion

This study reports the use of an FI to determine the prevalence of frailty in the rehabilitation setting and adds to the relatively limited New Zealand literature within this population. It is important that frailty tools used in different settings are shown to be fit for purpose and this FI derived from routinely collected electronic data demonstrated predictive validity in terms of six-month hospitalisation rates, one-year mortality and one-year LTC entry. Predictive validity is an essential component to frailty operationalisation, particularly as there is no gold-standard measurement.[[15,17]] The finding of increased frailty associated with shorter LOS is an unexpected finding and warrants further investigation. As the data sourced are electronically recorded, the potential exists to automatically generate a FS visible, and of use to, admitting clinicians without additional work—a point of difference to other clinically utilised tools.

Our cohort had high average baseline frailty (mean FI 0.42) and very high prevalence of frailty at 90.7% (utilising cut-off of 0.25).[[16]] Distribution was normal, which is to be expected in populations with greater health issues.[[17]] A study from Singapore[[18]] reported prevalence rate of 87% by FI of inpatients in a geriatrics department (although unlike our study, also including acute inpatients), while a study from a single rehabilitation facility in Switzerland 10 reported 44% were frail by FI (median 0.37). In contrast we can utilise mean FI score to compare to other studies, with the mean frailty in our cohort similar to that in two Australian studies[[19,20]] (mean FI 0.42 and 0.46) but higher than a Finnish study (0.34)[[21]] in similar rehabilitation settings. By comparing in this way, it appears older adults undergoing rehabilitation in our cohort sit at the higher end of frailty prevalence compared to international studies. Frailty prevalence in our study was higher than that reported by Richard et al.,[[13]] as far as we are aware the only other publication reporting frailty in the New Zealand rehabilitation setting. In Richard et al.’s point prevalence study of frailty in Christchurch hospital,[[13]] overall ~49% were considered frail by the Reported Edmonton Frailty Scale, increasing to 74% within the rehabilitation wards. Māori were significantly more likely to be frail.

Unsurprisingly, our subjects were found to be frailer than other New Zealand studies assessing frailty by FI in community settings.[[2,10]] The mean frailty by FI in a study of community dwelling older adults in Canterbury assessed for government-funded supports was 0.27, and 0.16 in a population of Auckland retirement village residents. While there are a small number of other New Zealand frailty studies utilising validated tools in specific sub-specialty populations, compared to our international colleagues, we in New Zealand are lacking in reports of frailty. This dearth of information, particularly as it affects our Indigenous population, has been noted.[[22]] Given healthcare costs are approximately five times higher for the frail compared to the non-frail,[[23]] it is essential that feasible strategies to identify those living with frailty are used and effective and appropriate interventions are delivered. From an overall community perspective, one way of potentially achieving this would be by improving accessibility to interRAI data.

Consistent with some, but not all similar reports, our study found association with discharge destination,[[19,20,24]] and one year mortality.[[24]] We found no significant association with short-term hospitalisations at 30 or 90 days. While the number of outcomes was small for 30-day hospitalisations, for 90 days it was comparable to other outcomes that showed significant differences. Other studies have assessed frailty with the need to be readmitted to acute care or the emergency department during their current rehabilitation admission.[[19–24]] This seems to infer medical instability and we did not study this as an outcome. There is little research in terms of outcomes for the rehabilitation population between frailty groups in the immediate post-discharge period; this is an area that requires further scholarship in larger studies. For example, it is possible that no association is found between frailty and short-term re-hospitalisation rates because older rehabilitation patients, usually cared for by geriatricians within a multidisciplinary team, receive a comprehensive geriatric assessment. Lin et al.[[26]] have recently published results showing frail older people are less likely to be readmitted if they received a comprehensive geriatrics assessment during admission. More study is required here, but perhaps geriatrician input prior to discharge eliminates some of the risk of readmissions for those living with a greater degree of frailty.

Our results differ significantly to the literature when assessing frailty association with LOS. Where other studies in both the acute setting[[27,28]] and (the smaller number) in the rehabilitation setting[[19,21]] show association of frailty with longer LOS, our results report the opposite with higher frailty associated with shorter LOS; a surprising result. We had expected the variance here to be explained by the higher number of participants already residing in LTC in the highest frailty group FS 5 (eight participants, out of 45) compared to FS 0–2, which had no participants already residing in LTC, and with high rates of FS 5 discharged to LTC overall, compared with only 7.1% of FS 0–2. Numbers were too small in these groups to show any significance; however, further subgroup analysis of LOS data found that the most frail, who were admitted from the community but discharged to LTC, had significantly shorter LOS, and that LOS decreased as frailty increased in this group. This may be a reflection of the frailest reaching their rehabilitation potential plateau or limits earlier than the less frail, with the decision to discharge made earlier, compared to those continuing to make inpatient gains. However, of potential concern, it may highlight an issue that the most frail and vulnerable population receive less physiotherapy or other allied health involvement due to a perceived lack of benefit. It may also reflect different practices between New Zealand and internationally with regards to the least and most frail patients. This requires further unravelling to ensure that best care is being delivered to all, and illustrates the value of interrogating and reporting frailty and outcomes in different populations and care settings.

Limitations

This study included a relatively small number of subjects from a single centre, and other outcomes may be significant if a larger cohort was included. Despite this, significant important findings were found.

Frailty in the older adult rehabilitation setting is relatively under-explored in comparison to acute hospitalised patients or community dwellers, yet it is an important group to be considered. The value of utilising electronic health data for FI development is the potential for automating FI results into clinical notes. This has potential to increase clinician awareness of this syndrome, including to primary care if FI is incorporated into discharge summaries. It brings frailty to the forefront, allowing focus on frailty-centred care and appropriate distribution of resources with evidence that geriatricians would use such information to inform clinical judgement and individualise care.[[29]]

Future focuses of study are to interrogate why the more frail have shorter LOS in this population by comparing components of frailty management, such as amount of physiotherapy received, between those more and less frail, and also to further investigate frailty level of short-term outcomes/readmission at time of discharge.

View Appendices.

Summary

Abstract

Aim

Frailty within the older adult rehabilitation population is relatively under-explored. We aimed to derive a frailty index (FI) from electronic routinely collected data to determine frailty prevalence, and to assess its ability to predict adverse outcomes in the rehabilitation setting.

Method

A FI was derived and retrospectively applied to electronically recorded health information of older adults admitted for inpatient rehabilitation. For analysis, subjects were allocated into frailty score (FS) groups (0–5). Primary outcome was a six-month hospitalistion rate, and other outcomes were: mortality, entrance into long-term care (LTC) at one year, length of stay (LOS), 30- and 90-day hospitalistions. Univariate and multivariable logistic regressions analysed associations between frailty and outcomes.

Results

One hundred and sixty-two patient electronic notes were reviewed. Mean (SD) age was 86 (8.2) years, 147 (90.7%) were considered frail (FS>0.25). The most frail group (FS 5) had higher risk of six-month hospitalisations (OR=6.19; 95%CI=1.82, 21.13; p=0.004). A higher frailty score was associated with shorter LOS compared to lowest frailty scores (15.7 days vs 25.4 days; p=0.04). No relationship was found with shorter-term outcomes.

Conclusion

Prevalence of frailty is high in the rehabilitation setting. Association of frailty with shorter LOS and lack of association found with shorter-term outcomes warrant further study.

Author Information

Himali Aickin: Waitematā District Health Board, Auckland, New Zealand. Katherine Bloomfield: Waitematā District Health Board, Auckland, New Zealand; The University of Auckland, Auckland, New Zealand. Zhenqiang Wu: The University of Auckland, Auckland, New Zealand. Martin J Connolly: Waitematā District Health Board, Auckland, New Zealand; The University of Auckland, Auckland, New Zealand.

Acknowledgements

Correspondence

Katherine Bloomfield: Waitematā Clinical Campus, North Shore Hospital, PO Box 93 503, Takapuna, Auckland 0740, New Zealand. Ph: +64 9 4868920/42414, Fax: +64 9 4427166

Correspondence Email

Katherine.bloomfield@waitematadhb.govt.nz

Competing Interests

Nil.

1) Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752-762. doi:10.1016/S0140-6736(12)62167-9.

2) Burn R, Hubbard RE, Scrase RJ, et al. A frailty index derived from a standardized comprehensive geriatric assessment predicts mortality and aged residential care admission. BMC Geriatr. 2018;18(1):319. doi:10.1186/s12877-018-1016-8.

3) Fried L, Tangen C, Walston J, et al. Frailty in Older Adults: Evidence for a Phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-M156. doi:10.1093/gerona/56.3.m146.

4) Dent E, Lien C, Lim WS, et al. The Asia-Pacific Clinical Practice Guidelines for the Management of Frailty [published correction appears in J Am Med Dir Assoc. 2018 Jan;19(1):94]. J Am Med Dir Assoc. 2017;18(7):564-575. doi:10.1016/j.jamda.2017.04.018.

5) Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173(5):489-495. doi:10.1503/cmaj.050051.

6) Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. ScientificWorldJournal. 2001;1:323-336. doi:10.1100/tsw.2001.58.

7) Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci. 2007;62(7):722-727. doi:10.1093/gerona/62.7.722.

8) Brousseau AA, Dent E, Hubbard R, et al. Identification of older adults with frailty in the Emergency Department using a frailty index: results from a multinational study. Age Ageing. 2018;47(2):242-248. doi:10.1093/ageing/afx168.

9) Hirdes JP, Ljunggren G, Morris JN, et al. Reliability of the interRAI suite of assessment instruments: a 12-country study of an integrated health information system. BMC Health Serv Res. 2008; 8:277-288. doi: 10.1186/1472-6963-8-277.

10) Bloomfield K, Wu Z, Tatton A, et al. An interRAI-derived frailty index is associated with prior hospitalisations in older adults residing in retirement villages. Australas J Ageing. 2021;40(1):66-71. doi:10.1111/ajag.12863.

11) Bloomfield K, Wu Z, Tatton A, Calvert C, Peel N, Hubbard R, Jamieson H, Hikaka J, Boyd M, Bramley D, Connolly MJ. An interRAI derived frailty index predicts acute hospitalizations in older adults residing in retirement villages: A prospective cohort study. PLoS One. 2022 Mar 2;17(3):e0264715.

12) Abey-Nisbit R, Peel NM, Matthews H, et al. Frailty of Māori, Pasifika and non- Māori/non-Pasifika older people in New Zealand: a national population study of older people referred for home care services. J Gerontol A Biol Sci Med Sci. 2021 May 22;76(6):1101-1107.

13) Richards SJG, D’Souza J, Pascoe R et al. Prevalence of frailty in a tertiary hospital: A point prevalence observational study. PLOS ONE 2019 Jul 1; 14(7):e0219083.

14) Bellelli G, Morandi A, Davis DHJ, et al. Validation of the 4AT, a new instrument for rapid delirium screening: A study in 234 hospitalised older people [published correction appears in Age Ageing. 2015 Jan;44(1):175]. Age Ageing. 2014;43(4):496-502. doi:10.1093/ageing/afu021.

15) Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8:24. doi:10.1186/1471-2318-8-24.

16) Rockwood K, Andrew M, Mitnitski A. A comparison of two approaches to measuring frailty in elderly people. J Gerontol A Biol Sci Med Sci. 2007 Jul;62(7):738-43.

17) Basic D, Shanley C. Frailty in older inpatient population: using the clinical frailty scale to predict patient outcomes. J Aging Health. 2015; 27:670-685.

18) Chong E, Ho E, Baldevarona-Llego J, Chan M, Wu L, Tay L. Frailty and risk of adverse outcomes in hospitalized older adults: a comparison of different frailty measures. J Am Med dir Assoc. 2017 Jul 1;18(7):638.e7-638.e11

19) Kohler S, Rametta R, Poulter M, Vogrin S, Yates P. Resilience, frailty and outcomes in geriatric rehabilitation. Austral J Aging. 2020 Jun;39)2)e205-e209.

20) Arjunan A, Peel NM, Hubbard RE. Feasibility and validity of frailty measurement in geriatric rehabilitation. Australas J Ageing. 2018 Jun;37(2):144-146.

21) Kerminen H, Huhtala H, Jäntti P, Valvanne J, Jämsen E. Frailty Index and functional level upon admission predict hospital outcomes: an interRAI-based cohort study of older patients in post-acute care hospitals. BMC Geriatr. 2020;20(1):160. Published 2020 May 5. doi:10.1186/s12877-020-01550-7.

22) Lewis ET, Howard L, Cardona M, Radford K, Withall A, Howie A, Rockwood K, Peters R. Frailty in Indigenous Populations: A Scoping Review. Front Public Health. 2021 Nov 22;9:785460.

23) Bock JO, König HH, Brenner H, Haefeli WE, Quinzler R, Matschinger H, Saum KU, Schöttker B, Heider D. Associations of frailty with health care costs--results of the ESTHER cohort study. BMC Health Serv Res. 2016 Apr 14;16:128.

24) Stuck AK, Mangold JM, Wittwer R, Limacher A, Bischoff-Ferrari HA. Ability of 3 Frailty Measures to Predict Short-Term Outcomes in Older Patients Admitted for Post-Acute Inpatient Rehabilitation. J Am Med Dir Assoc. 2021 Oct 20:S1525-8610(21)00865-3.

25) Chong E, Ho E, Baldevarona-Llego J, Chan M, Wu L, Tay, Ding YY, Lim WS. Frailty in hospitalized older adults: comparing different frailty measures in predicting short- and long-term patient outcomes. J Am Med Dir Assoc. 2018 May;19(5):450-457.e3.

26) Lin MH, Wang KY, Chen CH, Hu FW. Factors associated with 14-day hospital readmission in frail older patients: a case-control study. Geriatr Nurs. 2022 43;146-150.

27) Singh I, Gallacher J, Davis K, Johansen A, Eeles E, Hubbard RE. Predictors of adverse outcomes on an acute geriatric rehabilitation ward. Age Ageing. 2012;41:242-246.

28) Evans SJ, Sayers M, Mitnitski A, Rockwood K. The risk of adverse outcomes in hospitalized older patients in relation to a frailty index based on a comprehensive geriatric assessment. Age Ageing. 2014;43:127-132.

29) Khatry K, Peel NM, Gray LC, Hubbard RE. The Utility of the Frailty Index in Clinical Decision Making. J Frailty Aging. 2018;7(2):138-141.

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Frailty conceptualises a state of vulnerability due to multiple deficits across several physiological systems.[[1]] It has been shown to predict onset of disability, morbidity, entrance into long-term care (LTC) and mortality.[[1–3]] Identification of frailty can help guide treatments, prognosticate disease, and target resources toward modifiable parameters.[[4]]

There are several approaches to measuring frailty, but most screening tools fit into one, or a combination, of two broad categories: the phenotypic frailty model[[3]] and the cumulative deficit model.[[5–7]] The latter involves generating a frailty index (FI) by summing the deficits an individual has across a range of predetermined medical, functional and social parameters.[[5–7]] With increasing availability of electronic health data, the development of FIs to rapidly assess frailty is attractive.[[8]] Aotearoa New Zealand has been at the forefront of utilising routinely collected, electronically recorded data for FI development. These are attained using International Resident Assessment Instrument (interRAI)[[9]] assessments in those requiring government-funded community supports,[[2,10–12]] with these assessments being performed when patients are relatively well within the community. However, interRAI data are not currently readily accessible, with no access to developed electronic FIs within clinical settings in New Zealand. Additionally, interRAI assessments are only routinely performed in those requiring government-funded community supports, with these data not available for the many individuals requiring healthcare who have not had an interRAI assessment.

Frailty prevalence[[13]] and frailty-specific outcomes in older adults undergoing rehabilitation are not well described in New Zealand. Most rehabilitation units are led by geriatricians with a comprehensive geriatric assessment being integral to care provision, and therefore outcomes for these patients may be different to acutely hospitalised older adults. We wished to develop a tool to measure frailty and assess its predictive validity in the inpatient rehabilitation setting, using routinely electronically collected data.

Methods

This was a retrospective cohort study using routinely collected electronically recorded data in hospitalised adults aged ≥65 years, admitted to a rehabilitation unit at Waitematā District Health Board (WDHB), Auckland, New Zealand, from 8 May 2018 to 31 October 2018. Ethical approval was obtained from the New Zealand Central Health and Disability Ethics Committee (reference 19/CEN/128).

The WDHB rehabilitation service serves older adults aged ≥65 years who have medical and functional needs, providing comprehensive assessment, treatment and goal-directed rehabilitation on individual needs. The service has developed an electronically documented, readily accessible, concise global geriatric assessment—completed as part of the admission process to the inpatient rehabilitation service at WDHB. This electronic document captures active medical problems, comorbidities, medications, living situation, education level, social situation, cognition and mood, drugs and alcohol, vision and hearing impairment, bladder and bowel function, nutrition and appetite, pre-morbid personal and instrumental activities of daily living, and assistance required. It also captures a current 4AT rapid clinical test for delirium score to rapidly screen for delirium and cognitive impairment.[[14]]

Where a patient had more than one electronic admission document completed, only their first admission within the six-month period was included, so each individual was only represented once.

An FI was constructed by extracting variables from the electronic document (see Appendices). The chosen variables were based on previously developed FIs,[[2,5,15]] and study group consensus and encompassed domains of locomotion, sensory, cognition, psychological, function/ADLs and comorbidities. The majority of variables were coded using a binary system where 0 represents absence of the deficit, and 1 represents presence of the deficit. Certain variables were divided into more categories to delineate “degree of deficit”.

The FI numerator is a sum of points scored for each variable included in the FI, divided by the denominator.[[39]] Where information was unavailable these items were excluded from the total denominator, as per usual FI practice. The FI generated a value between 0 and 1, with higher values indicating more severe frailty.

Outcomes were measured one year after index admission by reviewing hospital electronic records and included six-month hospitalisations (primary outcome); one-year mortality; one-year entrance into long-term care (LTC); and 30-day and 90-day hospitalisation (secondary outcomes). Outcome data were sourced from electronic hospital data linked across the wider Auckland Region. The following items were also collected from hospital electronic records: age, gender, ethnicity, living alone/with others, marital status, residence on admission, primary diagnosis, length of stay (LOS) of index admission, discharge destination from index admission (home or LTC [rest home/private hospital/dementia unit]) and if this was a change from residence on admission.

To measure the association between FI and outcomes, participants were allocated into six groups, denoted frailty score (FS) 0,1,2,3,4,5 based on pre-defined FI ranges, consistent with FI application in community-dwelling older adults with health and functional needs, where FS 0=0.00–0.09, FS 1=0.10–0.19, FS 2=0.20–0.29, FS 3=0.30–0.39, FS 4=0.40–0.49, FS 5≥0.50.[[2]] Due to the small numbers of participants with lower FI levels in our study, the first three frailty groups (FS 0, 1 and 2) were combined. Analysis of variance (ANOVA) or Chi-squared tests were used to determine the association between pre-specified FI categories (FS 0–2, 3, 4, 5) and baseline characteristics. The univariate and multivariable association between FI categories (independent variable) and binary outcomes (dependent variables) were explored using logistic regression models with odds ratios (ORs) and 95% confidence intervals (95%CIs). A two-sided p<0.05 was considered statistically significant. All analyses were performed with SAS 9.4 software (SAS Institute Inc., Cary, USA).

Results

A total of 536 electronic admission documents were reviewed in the six-month study period, 369 were excluded as they were completed by non-rehabilitation services (e.g., orthogeriatrics). Five more were excluded as duplicates of the same individuals. In total, 162 participants were included in the analysis (see Figure 1).

Subjects were aged between 66 and 103 years, with a mean (SD)age of 86 (8) years. The median age was 88 years (interquartile range [IQR]=80–92).Two thirds (108) were female. The majority identified as NZ European (143; 88.3%) with 3 (1.9%) being Māori, and the cohort were largely either widowed or married (70, 43.2% and 66, 40.7% respectively). Increasing age was associated with increasing FS (Table 1).

The mean FI of the cohort was 0.42 (SD 0.12), ranging from 0.07 to 0.73, and was approximately normally distributed, as shown in Figure 2. There were 28 (17.3%) participants in FS groups 0–2, 39 (24.1%) in FS 3, 50 (30.9%) in FS 4, and 45 (27.7%) in FS 5, and 147 subjects were considered frail FI>0.25.[[16]]

The group with the highest frailty (FS 5) had the highest rate of being discharged to an increased level of care at 35.6% (n=16), compared with the least frail at 10.7% (n=3). The group with lowest frailty (FS 0–2) had the highest mean LOS at 25.4 days, and the group with the highest frailty (FS 5) had the lowest mean LOS of 15.7 days (p=0.04). Analysis of frailty category LOS by whether inpatients were admitted from LTC or home or whether they were discharged to a higher level of care (i.e., admitted from home, discharged to LTC) found that LOS was significantly shorter for the frailest being discharged to a higher level of care (Table 2).

At six months, a total of 80 (49.4%) participants had at least one hospitalisation. The six-month hospitalisation proportion was significantly different between FI groups; 66.7% in the most frail group compared to 35.7% in the least frail group (FS 0–2). Participants in the most fail group had significantly higher risk of hospitalisation in both unadjusted (OR=3.60; 95%CI=1.34, 9.70; p=0.01) and adjusted (OR=6.19; 95%CI=1.82, 21.13; p=0.004) logistic regressions (Table 3).

The overall one-year mortality proportion was 23% (n=37). One-year mortality in the composite group (FS 0–2) was 3.5% (n=1), significantly lower than FS 5 40.0% (n=18) (p=0.01; Table 3). In the adjusted logistic regression, similar results were observed (OR=14.69; 95%CI=1.58, 136.43; p=0.02).

At baseline 10 (6.2%) resided in LTC, eight of whom were in the most frail group (FS 5), and two participants in FS 3. These participants were excluded in the LTC admission analysis. By the end of the follow up period 51 (33.6%) of the remaining cohort had newly entered LTC. By one year 19 (54.1%) of the most frail group (FS 5) had newly entered LTC, compared to 4 (14.3%) of the composite group (FS 0–2) (p=0.004; Table 3). In the adjusted logistic regression similar results were observed in most frail group (OR=5.12; 95%CI=1.28, 20.43; p=0.02).

There were no significant differences in hospital admission proportion at 30 or 90 days between FI groups (Table 3).

View Tables and Figures.

Discussion

This study reports the use of an FI to determine the prevalence of frailty in the rehabilitation setting and adds to the relatively limited New Zealand literature within this population. It is important that frailty tools used in different settings are shown to be fit for purpose and this FI derived from routinely collected electronic data demonstrated predictive validity in terms of six-month hospitalisation rates, one-year mortality and one-year LTC entry. Predictive validity is an essential component to frailty operationalisation, particularly as there is no gold-standard measurement.[[15,17]] The finding of increased frailty associated with shorter LOS is an unexpected finding and warrants further investigation. As the data sourced are electronically recorded, the potential exists to automatically generate a FS visible, and of use to, admitting clinicians without additional work—a point of difference to other clinically utilised tools.

Our cohort had high average baseline frailty (mean FI 0.42) and very high prevalence of frailty at 90.7% (utilising cut-off of 0.25).[[16]] Distribution was normal, which is to be expected in populations with greater health issues.[[17]] A study from Singapore[[18]] reported prevalence rate of 87% by FI of inpatients in a geriatrics department (although unlike our study, also including acute inpatients), while a study from a single rehabilitation facility in Switzerland 10 reported 44% were frail by FI (median 0.37). In contrast we can utilise mean FI score to compare to other studies, with the mean frailty in our cohort similar to that in two Australian studies[[19,20]] (mean FI 0.42 and 0.46) but higher than a Finnish study (0.34)[[21]] in similar rehabilitation settings. By comparing in this way, it appears older adults undergoing rehabilitation in our cohort sit at the higher end of frailty prevalence compared to international studies. Frailty prevalence in our study was higher than that reported by Richard et al.,[[13]] as far as we are aware the only other publication reporting frailty in the New Zealand rehabilitation setting. In Richard et al.’s point prevalence study of frailty in Christchurch hospital,[[13]] overall ~49% were considered frail by the Reported Edmonton Frailty Scale, increasing to 74% within the rehabilitation wards. Māori were significantly more likely to be frail.

Unsurprisingly, our subjects were found to be frailer than other New Zealand studies assessing frailty by FI in community settings.[[2,10]] The mean frailty by FI in a study of community dwelling older adults in Canterbury assessed for government-funded supports was 0.27, and 0.16 in a population of Auckland retirement village residents. While there are a small number of other New Zealand frailty studies utilising validated tools in specific sub-specialty populations, compared to our international colleagues, we in New Zealand are lacking in reports of frailty. This dearth of information, particularly as it affects our Indigenous population, has been noted.[[22]] Given healthcare costs are approximately five times higher for the frail compared to the non-frail,[[23]] it is essential that feasible strategies to identify those living with frailty are used and effective and appropriate interventions are delivered. From an overall community perspective, one way of potentially achieving this would be by improving accessibility to interRAI data.

Consistent with some, but not all similar reports, our study found association with discharge destination,[[19,20,24]] and one year mortality.[[24]] We found no significant association with short-term hospitalisations at 30 or 90 days. While the number of outcomes was small for 30-day hospitalisations, for 90 days it was comparable to other outcomes that showed significant differences. Other studies have assessed frailty with the need to be readmitted to acute care or the emergency department during their current rehabilitation admission.[[19–24]] This seems to infer medical instability and we did not study this as an outcome. There is little research in terms of outcomes for the rehabilitation population between frailty groups in the immediate post-discharge period; this is an area that requires further scholarship in larger studies. For example, it is possible that no association is found between frailty and short-term re-hospitalisation rates because older rehabilitation patients, usually cared for by geriatricians within a multidisciplinary team, receive a comprehensive geriatric assessment. Lin et al.[[26]] have recently published results showing frail older people are less likely to be readmitted if they received a comprehensive geriatrics assessment during admission. More study is required here, but perhaps geriatrician input prior to discharge eliminates some of the risk of readmissions for those living with a greater degree of frailty.

Our results differ significantly to the literature when assessing frailty association with LOS. Where other studies in both the acute setting[[27,28]] and (the smaller number) in the rehabilitation setting[[19,21]] show association of frailty with longer LOS, our results report the opposite with higher frailty associated with shorter LOS; a surprising result. We had expected the variance here to be explained by the higher number of participants already residing in LTC in the highest frailty group FS 5 (eight participants, out of 45) compared to FS 0–2, which had no participants already residing in LTC, and with high rates of FS 5 discharged to LTC overall, compared with only 7.1% of FS 0–2. Numbers were too small in these groups to show any significance; however, further subgroup analysis of LOS data found that the most frail, who were admitted from the community but discharged to LTC, had significantly shorter LOS, and that LOS decreased as frailty increased in this group. This may be a reflection of the frailest reaching their rehabilitation potential plateau or limits earlier than the less frail, with the decision to discharge made earlier, compared to those continuing to make inpatient gains. However, of potential concern, it may highlight an issue that the most frail and vulnerable population receive less physiotherapy or other allied health involvement due to a perceived lack of benefit. It may also reflect different practices between New Zealand and internationally with regards to the least and most frail patients. This requires further unravelling to ensure that best care is being delivered to all, and illustrates the value of interrogating and reporting frailty and outcomes in different populations and care settings.

Limitations

This study included a relatively small number of subjects from a single centre, and other outcomes may be significant if a larger cohort was included. Despite this, significant important findings were found.

Frailty in the older adult rehabilitation setting is relatively under-explored in comparison to acute hospitalised patients or community dwellers, yet it is an important group to be considered. The value of utilising electronic health data for FI development is the potential for automating FI results into clinical notes. This has potential to increase clinician awareness of this syndrome, including to primary care if FI is incorporated into discharge summaries. It brings frailty to the forefront, allowing focus on frailty-centred care and appropriate distribution of resources with evidence that geriatricians would use such information to inform clinical judgement and individualise care.[[29]]

Future focuses of study are to interrogate why the more frail have shorter LOS in this population by comparing components of frailty management, such as amount of physiotherapy received, between those more and less frail, and also to further investigate frailty level of short-term outcomes/readmission at time of discharge.

View Appendices.

Summary

Abstract

Aim

Frailty within the older adult rehabilitation population is relatively under-explored. We aimed to derive a frailty index (FI) from electronic routinely collected data to determine frailty prevalence, and to assess its ability to predict adverse outcomes in the rehabilitation setting.

Method

A FI was derived and retrospectively applied to electronically recorded health information of older adults admitted for inpatient rehabilitation. For analysis, subjects were allocated into frailty score (FS) groups (0–5). Primary outcome was a six-month hospitalistion rate, and other outcomes were: mortality, entrance into long-term care (LTC) at one year, length of stay (LOS), 30- and 90-day hospitalistions. Univariate and multivariable logistic regressions analysed associations between frailty and outcomes.

Results

One hundred and sixty-two patient electronic notes were reviewed. Mean (SD) age was 86 (8.2) years, 147 (90.7%) were considered frail (FS>0.25). The most frail group (FS 5) had higher risk of six-month hospitalisations (OR=6.19; 95%CI=1.82, 21.13; p=0.004). A higher frailty score was associated with shorter LOS compared to lowest frailty scores (15.7 days vs 25.4 days; p=0.04). No relationship was found with shorter-term outcomes.

Conclusion

Prevalence of frailty is high in the rehabilitation setting. Association of frailty with shorter LOS and lack of association found with shorter-term outcomes warrant further study.

Author Information

Himali Aickin: Waitematā District Health Board, Auckland, New Zealand. Katherine Bloomfield: Waitematā District Health Board, Auckland, New Zealand; The University of Auckland, Auckland, New Zealand. Zhenqiang Wu: The University of Auckland, Auckland, New Zealand. Martin J Connolly: Waitematā District Health Board, Auckland, New Zealand; The University of Auckland, Auckland, New Zealand.

Acknowledgements

Correspondence

Katherine Bloomfield: Waitematā Clinical Campus, North Shore Hospital, PO Box 93 503, Takapuna, Auckland 0740, New Zealand. Ph: +64 9 4868920/42414, Fax: +64 9 4427166

Correspondence Email

Katherine.bloomfield@waitematadhb.govt.nz

Competing Interests

Nil.

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